Method for automatic community model generation based on uni-parity data

ABSTRACT

Method for automatic community model generation based on uni-parity data. Correlation analysis is employed to identify links within the community. Method may be particularized for solving specific problems such as determining the activities between individuals within a money laundering ring.

STATEMENT OF GOVERNMENT INTEREST

The invention described herein may be manufactured and used by or forthe Government of the United States for governmental purposes withoutthe payment of any royalty thereon.

BACKGROUND OF THE INVENTION

It can be very useful to know about activities between individuals. Forexample, what individuals are associated with other individuals? Whichindividuals communicate with other individuals? When two or moreindividuals get together is there an intended purpose? Who are theleaders or important individuals of a group? What is the organizationalstructure of the group? It can prove useful further yet to have thecapability to actually model the above types of interactions andassociations. To an extent, this type of social research has beenaddressed by employing the disciplines of data mining and communitygeneration.

Examples of such problems include mining movie data to find out howactors/actresses, directors, and producers are linked to differentmovies and how the movies are linked to different awards; mining on Webcommunity or topic related documents to find out where the hubs andauthorities or the related documents are and how they are linkedtogether; mining the commercial merchandise sales data of a franchisestore nation-wide to determine the associations (or correlations) amonga group of merchandise items; mining customer search topic datacollected over a period of time in a library to identify a group ofrelated common interests and their relationships; and mining the trafficdata collected from a wide network of geographical locations nation-wideor within a specific area (e.g., NY City) to find out the trafficaccident pattern correlations among a group of locations. The governmentor civilian sector also has a number of requirements for such acapability. Such examples include the identification of terrorist cells,crime rings such as money laundering, drug interdiction and theidentification of tactical units in the battlefield.

In some of the problems the data is given with existing links such asthe movie data with actor-movie links and the Web data with Web linkswhile in others the data is given completely in isolation and no linkinformation is available such as sales data, customer search topic datacollected from a library, or traffic records collected in differentgeographical locations. The goal then is to generate communities basedon yet-to-be-determined links between the data items. Current researchin community generation focuses on the former and is addressed under thearea of relational data mining and learning in the literature. But whathappens when you don't have explicit link/relationship information? Toour knowledge, nobody has systematically addressed this class ofproblems and in fact it has not even been identified as another paradigmwithin the community generation area let alone the data miningcommunity. To this avail, we have entitled this set of problems as theUni-party Data Community Generation (UDCG) problem. To facilitate thecomparison, we call the former class of problems (where we know or aregiven the relationships) as Bi-party Data Community Generation (BDCG)problems.

OBJECTS AND SUMMARY OF THE INVENTION

It is therefore an object of the present invention to provide amethodology for solving a uni-party data community generation paradigm.

A further object of the present invention is to provide a method whichemploys automatic community model generation for solving a uni-partydata community generation paradigm.

Yet another object of the present invention is to employ Link Discoverybased on Correlation Analysis (LDCA) for generating an automaticcommunity model.

A particular object of the present invention is to provide a method forsolving a Money Laundering Crime (MLC) case.

Briefly stated, the present invention provides a method for automaticcommunity model generation based on uni-parity data. Correlationanalysis is employed to identify links within the community. Method maybe particularized for solving specific problems such as determining theactivities with a money laundering ring.

A generalized embodiment of the present invention, method for automaticcommunity model generation based on uni-parity data, comprises the stepsof hypothesizing a subset S of set U, wherein for any pair of items insubset S there exists a mathematical function C applicable to the pairof items so as to generate a correlation value and correlationrelationship between any pair of items in subset S; generatingcorrelation values by applying the function C to each of the pairs ofitems in subset S; graphing G(S,E), wherein E is the edge set of graph Gwith computed correlation values as weights; and mapping graph G to oneof its subgraphs M⊂G so as to generate a community.

A further embodiment of the present invention, method for solving acommunity generation problem, comprises the steps of convertingdocuments to digital form and tagging the digitized documents; parsingthe digitized and tagged documents to extract the transaction historyvector for each individual; creating timelines of the transactionvectors so as to form a timeline map; determining the relevancy of thevectors; projecting the vectors along a time dimension so as to form ashistogram; translating the vectors into groups of activities byhistogram clustering; determining the local correlation between any pairof clusters in the timeline of two individuals; computing the globalcorrelations between pairs of individuals; converting data to a graph asa function of all individuals extracted from the documents and thecorrelation values between individuals; generating models based on asearch of all subgraphs with correlation values above a threshold; andoutputting a group model.

A particular embodiment of the present invention for solving a moneylaundering problem comprises applying the “one way nearest neighbor”principle, wherein the “one way nearest neighbor” principle furthercomprises that for every person's name encountered, the first immediatetime instance is the first time instance for a series of financialactivities; the second immediate time instance is the second timeinstance for another series of financial activities, etc.; for everytime instance encountered, all the subsequent financial activities areconsidered as the series of financial activities between this timeinstance and the next time instance; financial activities are identifiedin terms of money amount; money amount is neutral in terms of deposit orwithdrawal; each person's time sequence of financial activities isupdated if new financial activities of this person are encountered inother places of the same document or in other documents; and thefinancial activities of each time instance of a person is updated if newfinancial activities of this time instance of the same person areencountered in other places of the same document or in other documents.

To the accomplishment of the foregoing and related ends, the presentinvention, then, comprises the features hereinafter fully described andparticularly pointed out in the claims. The following description andthe annexed figures set forth in detail certain illustrative embodimentsof the invention. These embodiments are indicative, however, of but afew of the various ways in which the principles of the invention may beemployed. Other objects, advantages and novel features of the presentinvention will become apparent from the following detailed descriptionof the invention when considered in conjunction with the figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts the primary processes comprising a preferred embodimentof the present invention.

FIG. 2 depicts a block diagram process flow chart of an illustrativeexample of the preferred embodiment to solve a money laundering crimeproblem.

FIG. 3 depicts an event-driven, three-dimensional, nested data structurefrom the money laundering crime problem.

FIG. 4 depicts a timeline map from the three-dimensional, monetaryvector money laundering crime problem.

FIG. 5 depicts a clustering algorithm based on histogram segmentationfrom the money laundering crime problem.

FIG. 6 depicts an illustration of the algorithm to determine thecorrelation between two individuals from the money laundering crimeproblem.

DETAILED DESCRIPTION OF THE GENERALIZED EMBODIMENT

In this section, we propose a general methodology, called Link Discoverybased on Correlation Analysis (LDCA), as a solution to the generaluni-party data community generation problem. LDCA uses a correlationmeasure to determine the “similarity” of patterns between two data itemsto infer the strength of their linkage. The correlation measure may bedefined in fuzzy logic to accommodate the typical impreciseness of the“similarity” of patterns.

Referring to FIG. 1, the components of LDCA as well as the data flow ofthese components are depicted. In principle, LDCA consists of threebasic steps. For each problem in the uni-party data community generationparadigm, assume that the data item set is U. A Link Hypothesis step 100hypothesizes a subset S of U, such that for any pair of the items in Sthere exists a mathematical function (or a procedural algorithm) C thatapplies to this pair of items to generate a correlation value in therange of [0, 1], i.e., this step defines the correlation relationshipbetween any pair of items in S:∀p,qεS⊂U,C:S×S→[0,1]A Link Generation step 110 then applies the function C to every pair ofitems in S to generate the correlation values. This results in acomplete graph G(S,E) where E is the edge set of the graph with computedcorrelation values as the weights of the edges. Finally, a LinkIdentification step 120 defines another function P that maps thecomplete graph G to one of its subgraph M⊂G as a generated community.

AN ILLUSTRATIVE EXAMPLE OF THE PREFERRED EMBODIMENT MONEY LAUNDERINGCRIME

The Link Discovery based on Correlation Analysis (LDCA) methodology wasapplied to solving a specific community generation problem—theidentification of members within a Money Laundering Crime (MLC) Group.Specific algorithms are used in the LDCA process. Such algorithms havebeen implemented and tested in a prototype system which the presentinvention refers to as CORrelation AnaLysis (CORAL).

Preparing the Data

The input data to the MLC model generation problem is based on free textdocuments. The data is obtained from varying sources, such as bankstatements, financial transaction records, personal communicationletters (including emails), loan/mortgage documents, as well as otherrelated reports.

Referring to FIG. 2, the documents are converted 130 to a digital formatusing an OCR and key entities, (e.g., person names, organization names,financial transaction times and dates, location addresses, as well astransaction money amounts) are tagged 130 using an extraction tool usingXML. No link information is tagged, thereby making the problem anexcellent candidate for applying the LDCA methodology.

Once the data set is identified and acquired (i.e., obtained, convertedand tagged), it must be developed to define an internal data structure.Due to the nature of the data and the lack of detailed meta-like data, anumber of rules and assumptions are required. The rules and assumptionsto be applied by the present invention are:

-   -   The data set U is the set of all extracted individuals from the        collection of the given documents.    -   For each individual, there is a corresponding financial        transaction history vector (may be null) along timeline.    -   The correlation between two individuals is defined through a        correlation function between the two corresponding financial        transaction history vectors.    -   If two individuals are in the same MLC group, they should        exhibit similar financial transaction patterns, and thus, should        have a higher correlation value.    -   Any two individuals may have a correlation value (including 0),        i.e., S=U.        Since the present invention has access to only the isolated and        tagged entities in the document, assumption must be made to        reasonably “guess” the associated relationships between the        extracted time/date stamps and the money amount of a specific        transaction with the extracted individual. Therefore, when the        present invention parses 140 the collection of documents to        extract the financial transaction history vectors for every        individual, it follows the “one way nearest neighbor” principle:    -   For every person's name encountered, the first immediate time        instance is the first time instance for a series of financial        activities; the second immediate time instance is the second        time instance for another series of financial activities, etc.    -   For every time instance encountered, all the subsequent        financial activities are considered as the series of financial        activities between this time instance and the next time        instance.    -   Financial activities are identified in terms of money amount;        money amount is neutral in terms of deposit or withdrawal.    -   Each person's time sequence of financial activities is updated        if new financial activities of this person are encountered in        other places of the same document or in other documents. The        financial activities of each time instance of a person is        updated if new financial activities of this time instance of the        same person are encountered in other places of the same document        or in other documents.

Based on the rules described above, whenever a new individual's name isencountered, a new PERSON event is created (see FIG. 3); whenever a newtime instance is encountered, a new TIME event is created under a PERSONevent (see FIG. 3); whenever a new financial transaction is encountered,a new TRANSACTION event is created linked to both corresponding TIME andPERSON events (see FIG. 3). All the events are represented as vectors.FIG. 3 depicts the data structure created by the present invention.

Still referring to FIG. 2, timelines are created 150 as a result ofparsing 140 the entire collection of documents and using the given datastructure. Each timeline (see FIG. 4) represents the financialtransaction history vector of each individual. The time axis of thetimelines is divided into discrete time instances. Each node in thetimelines is called a “monetary vector” that records the part of thefinancial transaction history of the corresponding person between thecurrent time instance and the next time instance.

While the above “one way nearest neighbor” parsing principle may not benecessarily true in all the circumstances, it is believed to be the bestfor the following two reasons: (1) this is the best outcome in theabsence of the actual association information in the data; (2) theexperimental evaluations show that the generated models based on thisprinciple are reasonably accurate.

The next part of this step is to determine relevancy 160 or, determinewhich monetary vectors are “useful”, i.e., is an individual related tothe money laundering case being investigated, and which vectors are justnoise (e.g., a “normal” financial transaction of an individual such as a“normal” purchasing activity, or a false association between one'smonetary activity and someone else due to the one way nearest neighborparsing principle). Since the present invention does not know therelevancy of the data, a “guess” must be made. During the datacollection process the investigators typically have the intention tocollect all the documents that are related to suspects in the case, orthose either suspiciously or routinely related to the case; thus, it isexpected that for those individuals who might be involved in the crimes,the majorities of their monetary vectors should be well clustered intoseveral “zones” in the timeline axis (see FIG. 4) where the actual MLCsare committed. This assumption is referred to as the “focus” assumption.Based on the focus assumption, the present invention needs to payattention to only the “clusters” of the monetary vectors in the timelinemap, and can ignore those monetary vectors that are scattered over otherplaces of the timeline map. This allows maximum filtering of the noisewhen determining the correlation between two individuals.

The present invention next projects 170 all the monetary vectors of allthe individuals into the timeline axis to form a histogram (see FIG. 5).Consequently, the clustering problem is reduced to a segmentationproblem in the histogram to divide the entire timeline into differenttime zones, or called groups of activities 180.

A histogram is generated (see FIG. 5) from all the monetary vectorsalong the timeline. Since the projection and the histogram segmentationmay be performed in linear time in the timeline space, this clusteringalgorithm significantly improves the complexity and avoids the iterativesearch a “normal” clustering algorithm such as the K-means algorithmwould typically require. The resulted number of “hills” (i.e., segments)in the histogram becomes the K clusters or time zones as groups ofactivities.

Link Hypothesis

At this point the present invention has formatted the data in a mannerin which it can compute correlation values 200 among pairs of people.After clustering, each individual's financial transaction history vectormay be represented as a timeline histogram partitioned into K clusters.The K clusters may in turn be represented as K histogram functions oftime t: <f_(i)(t)>, (where f_(i)(t) is the financial transactionhistogram of this individual in cluster i). The correlation between twoindividuals <x,y> is defined as an combined global correlation of allthe local correlations between the two individuals, whereas the localcorrelation is defined as the correlation between two clusters of thetimeline histograms of the two individuals.

Global correlation is determined 200 from local correlations between twoindividuals x and y (see FIG. 6). The correlation is defined as this“two level” function due to the unique nature of the problem, i.e.,individuals in the same MLC group may exhibit similar financialtransaction patterns in different time “zones” (which constrains thelocal correlation), but the difference in the timeline of theirfinancial activities should not be too large (which constrains theglobal correlation). While the local correlation is defined following astandard approach in Pattern Recognition literature to determining afuzzified “similarity” between two functions, the global correlation isdefined based on the unique nature of this problem to further constrainthe overall “similarity” between the financial transaction patternsalong the timeline of two individuals.

In defining a reasonable correlation function, it should be noted thatthe concept of similar financial transaction patterns is always fuzzy.That is to say, if two individuals belong to the same crime group andare involved in the same MLC case, it is unlikely that they wouldconduct transactions related to the crime simultaneously at the exacttime, nor is it likely that they would conduct transactions related tothe crime at times that are of a year difference. It would be likelythat they conduct the transactions at two different times close to eachother. Consequently, we apply fuzzy logic in both definitions of thelocal and global correlations to accommodate the actual “inaccuracy” ofthe occurrences in the extracted financial transaction activitiesbetween different individuals at different times.

Local Correlation

The present invention defines fx_(i)(t) and fy_(j)(t) be the financialtransaction histogram functions of individual x and y in cluster i andj, respectively. Following the standard practice to define a fuzzifiedcorrelation between two functions, it then uses the Gaussian function asthe fuzzy resemblance function within cluster i between time instance aand b:${G_{i}\left( {a,b} \right)} = {\frac{1}{\sqrt{2{\pi\sigma}_{i}}}e^{- \frac{{({a - b})}^{2}}{2\sigma_{i}^{2}}}}$$\quad{\sigma_{i} = {\frac{2}{W_{i}\left( {W_{i} - 1} \right)}{\sum\limits_{a = 1}^{W_{i}}\quad{\sum\limits_{b = {a + 1}}^{W_{i}}{{a - b}}}}}}$where σ_(i) is defined accordingly based on the specific context in thisproblem, and W_(i) is the width of the cluster i.

The Gaussian function is used because it gives a natural decay over thetime axis to represent the fuzzy resemblance between two functions.Consequently, two transactions of two individuals which occurred atcloser times results in more resemblance than those which occurred atfarther away times. It can be shown that after applying the fuzzy logicusing the Gaussian function as the resemblance function, the resultingfuzzified histogram is the original one convolved with the fuzzyresemblance function.${{gx}_{i}(t)} = {\sum\limits_{t^{\prime} = 1}^{W_{i}}{{{fx}_{i}\left( t^{\prime} \right)}{G_{i}\left( {t,t^{\prime}} \right)}}}$Thus, determining the local correlation 190 between fx_(i)(t) andfy_(i)(t) is defined as determining the maximum convolution value${g\left( {x_{i},y_{j}} \right)} = {\max_{t = 0}^{W_{i}}{\sum\limits_{t^{\prime} = {- W_{j}}}^{W_{j}}\quad{{{gx}_{i}\left( t^{\prime} \right)}{{gy}_{j}\left( {t - t^{\prime}} \right)}}}}$Global Correlation

The present invention assumes that the timeline axis is clustered into Ksegments. Based on the definition of the local correlation 190, for eachindividual x, at every cluster i, there is a set of K local correlationswith individual y {g(x_(i), y_(j)), j=1, . . . , K}. It then assigns thefuzzy weights to each of the elements of the set based on anotherGaussian function to accommodate the rationale that strong correlationsshould occur between financial transactions of the same crime groupcloser in time than those farther away in time. Thus, the followingseries results:{g(x_(i), y_(j))S(i,j), j=1, . . . , K}where${S\left( {i,j} \right)} = e^{- \frac{{({c_{i} - c_{j}})}^{2}}{2\sigma_{i}^{2}}}$and c_(i) and c_(j) are the centers of cluster i and cluster j along thetimeline.

The correlation between individual x in cluster i and the wholefinancial transaction histogram of individual y is then defined based onthe winner-take-all principle:C(x _(i) ,y)=max_(j=1) ^(K) {g(x _(i) ,y _(j))S(i,j)}Defining the vectorsCy(x)=<C(x _(i) ,y), i=, . . . , K>Cx(y)=<C(y _(i) ,x), i=1, . . . , K>then computing global correlation 200 between x and y is defined bycomputing the dot product between the two vectors:${C\left( {x,y} \right)} = {{{{Cy}(x)} \cdot {{Cx}(y)}} = {\sum\limits_{i = 1}^{K}\quad{{C\left( {x_{i},y} \right)}{C\left( {y_{i},x} \right)}}}}$Link Generation

After applying the correlation function to determine the globalcorrelation 200 to every pair of individuals in the data set U, thepresent invention obtains a complete graph G(V, E) 210, where V is theset of all the individuals extracted from the given collection of thedocuments, and E is the set of all the correlation values betweenindividuals such that for any correlation C(x, y), there is acorresponding edge in G with the weight C between the two nodes x and y.

Link Identification

For the problem of MLC group model generation 220, the present inventiondefines the function P in Link Identification as a graph segmentationbased on a minimum correlation threshold T. The specific value of T maybe obtained based on a user's expertise (in this example a lawenforcement investigator), which allows the user to validate differentmodels based upon different thresholds and their expertise. Note thatthere may be multiple subgraphs M generated based on different values ofT, indicating that there may possibly be multiple MLC groups identifiedin the given document collection. It is also possible that the originalgraph G(V, E) may not necessarily be connected (the complete graph G mayhave edges with correlation values 0, resulting in virtually anincomplete graph). Lastly, the generated models are output 230.

While the preferred embodiments have been described and illustrated, itshould be understood that various substitutions, equivalents,adaptations and modifications of the invention may be made thereto bythose skilled in the art without departing from the spirit and scope ofthe invention. Accordingly, it is to be understood that the presentinvention has been described by way of illustration and not limitation.

1. Method for automatic community model generation based on uni-paritydata, comprising the steps of: hypothesizing a subset S of set U,wherein for any pair of items in said subset S there exists amathematical function C applicable to said pair of items so as togenerate a correlation value and correlation relationship between anysaid pair of items in subset S; generating said correlation values byapplying said function C to each of said pairs of items in said subsetS; graphing G(S,E), wherein E is the edge set of said graph G withcomputed correlation values as weights; and mapping said graph g to oneof its subgraphs M⊂G so as to generate a community.
 2. Method of claim1, wherein said correlation relationship and said correlation value isdefined by:∀p,qεS⊂U,C:S×S→[0,1]and wherein said correlation value is in the rangeof [0,1].
 3. Method for solving a community generation problem,comprising the steps of: converting documents to digital form andtagging said digitized documents; parsing said digitized and taggeddocuments to extract the transaction history vector for each individual;creating timelines of said transaction vectors so as to form a timelinemap; determining the relevancy of said vectors; projecting said vectorsalong a time dimension so as to form a histogram; translating saidvectors into groups of activities by histogram clustering; determiningthe local correlation between any pair of clusters in the timeline oftwo individuals; computing the global correlations between pairs ofindividuals; converting data to a graph as a function of all individualsextracted from said documents and the correlation values between saidindividuals; generating models based on a search of all subgraphs withcorrelation values above a threshold; and outputting a group model. 4.Method of claim 3, wherein said step of parsing further comprises thestep of applying the “one way nearest neighbor” principle.
 5. Method ofclaim 4, wherein said “one way nearest neighbor” principle furthercomprises the following steps as applied to a money laundering problem:for every person's name encountered, the first immediate time instanceis the first time instance for a series of financial activities; thesecond immediate time instance is the second time instance for anotherseries of financial activities, etc.; for every time instanceencountered, all the subsequent financial activities are considered asthe series of financial activities between this time instance and thenext time instance; financial activities are identified in terms ofmoney amount; money amount is neutral in terms of deposit or withdrawal;each person's time sequence of financial activities is updated if newfinancial activities of this person are encountered in other places ofthe same document or in other documents; and the financial activities ofeach time instance of a person is updated if new financial activities ofthis time instance of the same person are encountered in other places ofthe same document or in other documents.
 6. Method of claim 3, whereinsaid step of determining the relevancy of said vectors further comprisesa step of focusing on “clusters” of vectors in said timeline map andignoring scattered (i.e., non-clustered) vectors in said timeline map.7. Method of claim 3, wherein said step of translating said vectors intogroups of activities further comprises solving a standard histogramclustering problem; and simplifying said standard clustering problem byvirtue of all individuals sharing the same said timeline.
 8. Method ofclaim 3, wherein said step of computing correlations between pairs ofindividuals further comprises computing the global correlation of alllocal correlations between pairs of individuals.
 9. Method of claim 8,further comprising the step of computing local correlations by computingthe correlation between two clusters corresponding to a pair ofindividuals on said histograms.
 10. Method of claim 9, wherein said stepof computing correlations between two clusters further comprises thestep of computing the fuzzified correlation between fx_(i)(t) andfy_(j)(t), the financial transaction histogram functions of individual xand y in cluster i and j, respectively.
 11. Method of claim 10, whereinsaid step of computing the fuzzified correlation between fx_(i)(t) andfy_(j)(t) further comprises the step of computing the maximumcorrelation value${g\left( {x_{i}y_{j}} \right)} = {\max_{t = 0}^{W_{i}}{\sum\limits_{t^{\prime} = {- W_{j}}}^{W_{j}}\quad{{{gx}_{i}\left( t^{\prime} \right)}{{gy}_{j}\left( {t - t^{\prime}} \right)}}}}$${{where}\quad{{gx}_{i}(t)}} = {\sum\limits_{t^{\prime} = 1}^{W_{i}}\quad{{{fx}_{i}\left( t^{\prime} \right)}{G_{i}\left( {t,t^{\prime}} \right)}}}$${{where}\quad{G_{i}\left( {a,b} \right)}} = {\frac{1}{\sqrt{2\pi}\sigma_{i}}e^{- \frac{{({a - b})}^{2}}{2\sigma_{i}^{2}}}}$${{and}\quad{where}\quad\sigma_{i}} = {\frac{2}{W_{i}\left( {W_{i} - 1} \right)}{\sum\limits_{a = 1}^{W_{i}}{\sum\limits_{b = {a + 1}}^{W_{i}}{{a - b}}}}}$12. Method of claim 8 wherein said step of computing the globalcorrelation of all local correlations between pairs of individualsfurther comprises computing the dot product between two vectors asfollows:${C\left( {x,y} \right)} = {{{{Cy}(x)} \cdot {{Cx}(y)}} = {\sum\limits_{i = 1}^{K}\quad{{C\left( {x_{i},y} \right)}{C\left( {y_{i},x} \right)}}}}$where the vectors Cy(x) and Cx(y) are defined asCy(x)=<C(x _(i) ,y), i=1, . . . , K>Cx(y)=<C(y _(i) ,x), i=1, . . . , K>where C(x _(i) ,y)=max_(j=1) ^(K) ={g(x _(i) ,y _(j))S(i,j)}${S\left( {i,j} \right)} = e^{- \frac{{({c_{i} - c_{j}})}^{2}}{2\sigma_{i}^{2}}}$and where{g(x_(i),y_(j))S(i,j), j=1, . . . , K}
 13. Method of claim 3, whereinsaid step of converting data to a graph further comprises obtaining acomplete graph G(V, E), where V is the set of all the individualsextracted from the given collection of the documents, and E is the setof all the correlation values between individuals such that for anycorrelation C(x, y), there is a corresponding edge in G with the weightC between the two nodes x and y.
 14. Method of claim 3, wherein saidstep of generating models further comprises the step of identifyinglinks as a graph segmentation based on a minimum correlation thresholdvalue.
 15. Method of claim 14, wherein said minimum threshold value isselected based upon a user's expertise.